{"title":"关于学习空间的评论","authors":"Xun Ge","doi":"10.1016/j.jmp.2024.102890","DOIUrl":null,"url":null,"abstract":"<div><div>This paper discusses learning spaces in the sense of Eppstein et al. (2008) to show that: (1) a learning space need not to have a base; (2) an essentially finite learning space need not to be well-graded; (3) the positive content family of a closed rooted medium need not to be a knowledge structure, and so it need not to be a learning space. These results disprove three assertions in Eppstein et al. (2008).</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"123 ","pages":"Article 102890"},"PeriodicalIF":2.2000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remarks on learning spaces\",\"authors\":\"Xun Ge\",\"doi\":\"10.1016/j.jmp.2024.102890\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper discusses learning spaces in the sense of Eppstein et al. (2008) to show that: (1) a learning space need not to have a base; (2) an essentially finite learning space need not to be well-graded; (3) the positive content family of a closed rooted medium need not to be a knowledge structure, and so it need not to be a learning space. These results disprove three assertions in Eppstein et al. (2008).</div></div>\",\"PeriodicalId\":50140,\"journal\":{\"name\":\"Journal of Mathematical Psychology\",\"volume\":\"123 \",\"pages\":\"Article 102890\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mathematical Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022249624000592\",\"RegionNum\":4,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mathematical Psychology","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022249624000592","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
This paper discusses learning spaces in the sense of Eppstein et al. (2008) to show that: (1) a learning space need not to have a base; (2) an essentially finite learning space need not to be well-graded; (3) the positive content family of a closed rooted medium need not to be a knowledge structure, and so it need not to be a learning space. These results disprove three assertions in Eppstein et al. (2008).
期刊介绍：
The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome.
Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation.
The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology.
Research Areas include:
• Models for sensation and perception, learning, memory and thinking
• Fundamental measurement and scaling
• Decision making
• Neural modeling and networks
• Psychophysics and signal detection
• Neuropsychological theories
• Psycholinguistics
• Motivational dynamics
• Animal behavior
• Psychometric theory
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